Saurabh’s Original Framework
Context Engineering Framework
Great AI outputs start with great context, not just great prompts.
Developed by Saurabh Jaiswal as part of the AI for Real Work methodology, this framework helps teams design AI systems around intent, memory, retrieval, workflow state, and trusted knowledge.
Core idea
Prompt engineering focuses on asking better questions. Context engineering focuses on giving AI the right situation, data, memory, rules, and task state before expecting useful answers.
The 5 layers
Layer 1
Intent
Understand what the user is really trying to achieve.
Layer 2
Context
Bring business rules, constraints, history, and goals into the AI flow.
Layer 3
Knowledge
Connect trusted sources, documents, data, and retrieval systems.
Layer 4
Workflow State
Track where the user is in the real task or process.
Layer 5
Feedback
Use human correction and system learning to improve future outputs.
Why it matters
Most AI failures are not only model failures. They are context failures: missing data, unclear intent, wrong assumptions, no memory, or no workflow awareness.
Real-world use
Use this framework for RAG systems, enterprise copilots, AI agents, customer support assistants, internal knowledge systems, and workflow automation.
Final takeaway
Better prompts can improve answers. Better context can improve the entire AI system.